LSEG Data & Analytics Market Data

Most market-data products sell you one feed and let you imagine the rest. LSEG Data & Analytics Market Data is the opposite problem: an estate so wide that the hard part is choosing the slice. Published by London Stock Exchange Group, the offering spans more than 80 petabytes of tick history reaching back to 1996, 80 million financial instruments across asset classes, 53 million economic indicators and instruments, and over 4 million Reuters stories a year. Everything is filed into seven workflow categories - trading workflows; pricing, risk and regulatory; research and modelling; news and text analytics; wealth investor experiences; modernised data delivery; and data redistribution.

The named products give the catalogue its shape. Tick History - PCAP is described as a cloud-based, 20+ petabyte repository of ultra-high-quality global market detail captured at the data-centre level. The Real-Time Managed Distribution Service handles cloud-based integration, publication, distribution and analytics. The LSEG Pricing Service supplies independent evaluated marks across more than 3 million fixed income securities, derivatives and bank loans - the bond, loan and derivative corners where printed values go sparse. Around those sit reference data for front-, middle- and back-office operations, LSEG Quantitative Analytics research databases, StarMine financial modelling, Machine Readable News and Text Analytics with historical archives, the LSEG data visualiser, embeddable widgets and DataScope Warehouse, packaged as ready-to-use enterprise extracts.

In Datadory's catalog of 1,744 datasets across 159 viable industries, this record scores 7/10 - a band shared by 296 records against a catalog-wide average of 7.81 - and it sets the scale ceiling of the financial exchanges shelf. Get a sample of this dataset and we cut the estate down to the products, fields and history your job actually needs.

LSEG does not publish row-level samples on its public surface, so the honest sample is the record itself - our catalog entry exactly as the August 2026 research pass logged it:

id                   financial-exchanges-data--lseg-data-analytics-market-data
name                 LSEG Data & Analytics Market Data
publisher            London Stock Exchange Group (LSEG)
industry             Financial Exchanges & Data
quality_score        7 / 10
estate_scale         80+ petabytes of tick history back to 1996;
                     80 million instruments; 53 million economic
                     indicators and instruments; 4 million+ Reuters
                     stories per year
named_products       Tick History - PCAP; Real-Time Managed
                     Distribution Service; LSEG Pricing Service;
                     DataScope Warehouse; StarMine
geography            Global - London, Milan, Stockholm, Copenhagen
                     and Oslo venues plus worldwide markets and FX
grain                per instrument per tick (PCAP);
                     per instrument per day (evaluated, reference)
dictionary_fields    4 (confidence: inferred)

Fifteen attributes, one institution, no invented numbers. Every figure above traces to the publisher's own published scale markers; nothing is extrapolated from marketing prose. Expand one product inside the estate and the four documented dictionary fields attach to it directly:

product_name         Tick History - PCAP
solution_category    one of seven workflow buckets, trading
                     workflows through data redistribution -
                     bucket assignment not published per product
coverage_statistic   20+ petabytes of global market detail,
                     captured at the data-centre level
delivery_channel     APIs, files, feeds, cloud-native or
                     enterprise platforms

That second block is where most buyers start, because it shows the three decisions the catalogue keeps asking you to make: which workflow bucket, which magnitude of coverage, which route of delivery. Name the products you want in your sample and the same card comes back populated for each.

Four structures are documented down to the enum level, recorded during our August 2026 research pass with confidence marked inferred - the publisher documents its offerings in prose rather than publishing a formal schema, so the dictionary describes what each field carries rather than guaranteeing a wire format. The table below is the spine. Per-product field layouts - the PCAP tick schema among them - were not individually verifiable this session, so we confirm them against your sample before any production delivery. Ask for a specific product by name and its attributes fold into the same table rather than arriving as a separate document.

Three chips, drawn straight from the record:

• Geography - global, including all London Stock Exchange Group venues (London, Milan, Stockholm, Copenhagen, Oslo and others) plus worldwide markets and FX.
• Temporal - tick history accumulating since 1996, with PCAP holding 20+ petabytes of the wider 80+ petabyte estate; evaluated and reference records run on a per-instrument-per-day clock.
• Granularity - per instrument per tick in the PCAP repository, per instrument per day for evaluated and reference records, per entitlement per user for distribution products.

Scale check: 80 million instruments and 53 million economic indicators and instruments make this the largest single estate on Datadory's financial exchanges shelf - an archive most vendors carve up and resell as separate products sits here under one catalogue, one vocabulary, seven workflow buckets.

API, files, or your warehouse. Daily, weekly, or hourly.

The tables land however your stack wants them - pushed to storage, served over an endpoint, or synced straight into your database, as JSON or CSV. Name the products, the field cut and the cadence when you request the sample; the sample ships first either way. Get a sample of this dataset and the catalogue card above arrives alongside the full dictionary for whichever slice you picked.

A catalogue this deep earns its keep in specific jobs:

• Run execution research on venue-grade history - tick-by-tick detail since 1996 lets desks reconstruct how orders actually behaved, market by market, instead of inferring behaviour from bars. See investors and quants use cases.
• Price the corners where markets go quiet - independent evaluated marks across 3 million-plus fixed income securities, derivatives and bank loans fill the gaps printed values leave on illiquid bonds and loans.
• Build features once capacity is running - multi-decade tick history plus machine-readable news and text archives feed pipelines that need exogenous signal beside price. See data scientists use cases.
• Profile the market-data industry itself - the published scale markers (80+ petabytes, 80 million instruments, 4 million+ stories a year) are the numbers analysts quote when sizing the vendor landscape.
• Benchmark a competing data service - the bundle of distribution, tick-history, pricing and reference products is the yardstick rivals get measured against.
• Put multi-venue history inside a product - embeddable widgets, the data visualiser and redistribution tooling exist for teams whose own software needs the content embedded rather than analysed. See developers and builders use cases.

Six of Datadory's eight personas tag this record, led harder than anything else in the slice:

• Investors & quant researchers (relevance 3/3) - venue-grade tick history since 1996 and evaluated bond marks are the backbone of execution research and fixed-income work at institutional budgets.
• Data scientists & ML engineers (2/3) - once capacity is running, the pull is long-history detail plus machine-readable news sitting beside price in the same catalogue.
• Market researchers (1/3) - the scale markers double as industry-sizing evidence when profiling the market-data sector itself.
• Competitive-intel & product teams (1/3) - positioning a rival data service means benchmarking against this bundle, feature by feature.
• Developers & data-product builders (1/3) - integration surfaces exist at enterprise grade, planned around provisioning lead time rather than instant setup.
• Journalists, academics & students (1/3) - multi-decade, multi-venue history supports market-structure reporting and research that a single ticker's quotes cannot carry.

Three things worth deciding upfront. First, breadth versus depth: the estate spans tick history, evaluated marks, reference records, news archives and modelling databases as separate named products, so say which ones your job touches and the sample gets cut to those instead of a tour of everything. Second, grain: PCAP runs per instrument per tick while evaluated and reference records run per instrument per day, so tell us whether your model eats ticks or daily marks and we shape rows accordingly. Third, honesty about verification: our August 2026 research pass verified the publisher's own scale figures and catalogue structure, but per-product schemas were not individually documented on the public surface, so field-level confirmation happens against your sample before any production delivery.

One structural note: the seven workflow buckets organise how buyers shop, not how the tables join. Instrument identifiers carry across products, which is why a tick-history pull and an evaluated-mark pull merge cleanly once both arrive cut to matching symbols and dates.

Notes worth reading next:

• Financial exchanges data data hub - the pooled view of the industry slice, eleven primary datasets deep, from commercial market-data suites to official statistics.
• Euronext Market Data vs LSEG Data & Analytics Market Data - venue-native European depth against global scale; both records score 7/10, and the scored trade-offs are set out row by row.
• Massive (formerly Polygon.io) Stock Market API - nanosecond US tick history when the job is US equities and options specifically rather than the whole multi-asset estate.
• Tiingo Financial Markets API - corporate-action-adjusted end-of-day prices for 109,725 securities back to 1962 when clean daily history beats tick depth.
• Nasdaq Data Link (formerly Quandl) - Sharadar, Mergent and Zacks tables behind one marketplace for fundamental factor work.
• LSEG FTSE Russell source profile - the index side of the same group, including the FTSE Nareit US Real Estate Index Series.
• Best financial exchanges datasets - where this record ranks within the slice and what beats it for other jobs.
• Tick-level market data and reference data - the two terms this page leans on hardest, unpacked.